In logistics, every route, every customer, and every service level has a different true cost. Without knowing what that cost is, your pricing is systematically wrong.
Logistics and distribution businesses operate on thin margins, high fixed costs, and significant complexity. A single regional distribution network may serve hundreds of customers across dozens of routes, with different service level agreements, different freight profiles, and completely different cost-to-serve characteristics.
The problem is that most logistics companies price based on market rates, historical contracts, or simple per-kg / per-pallet rates that do not reflect the true cost of service. The result is systematic cross-subsidisation: low-complexity, high-density routes subsidise complex, low-density ones. High-frequency small-order customers are chronically underpriced relative to their actual cost-to-serve.
Standard costing approaches systematically distort profitability in logistics. Here is why.
A customer receiving 5 pallets on a regular weekly schedule to a single location has a completely different cost-to-serve than a customer receiving 3 deliveries per week with variable orders, multiple drop points, and tight time windows - even if the freight volume is similar.
Most logistics companies know their overall margin. Very few know which routes are profitable, which are marginal, and which are loss-making. Without route-level profitability, network optimisation decisions are made without the data they require.
Warehouse fixed costs, vehicle depreciation, and driver costs are often spread across customers by volume. This penalises your highest-volume customers and subsidises low-volume, high-touch accounts - systematically distorting the commercial case for each.
How to build an accurate cost model for logistics that captures complexity, scales with volume, and drives real decisions.
Identify all the activities that consume cost in your network: order receipt and processing, warehouse pick/pack, loading, transport by route segment, last-mile delivery, returns handling, invoicing, and customer service. Each has a resource cost and a measurable driver.
Calculate the cost per km, cost per drop, cost per pallet position, and cost per driver hour for each segment of your network. These rates become the building blocks for customer and contract-level costing.
For each customer or contract, build a cost model that reflects their actual service profile: order frequency, average order size, number of delivery locations, time window requirements, returns rate, and customer service interactions. Sum the activity costs to get true cost-to-serve.
Compare cost-to-serve against contracted revenue for each account. Rank customers from most to least profitable. Apply the same logic to routes and service level categories. This is your logistics Whale Curve.
When you apply accurate cost-to-serve analysis in logistics, these findings are typical.
In most distribution networks, when true cost-to-serve is calculated, 15-25% of customers generate negative contribution. These accounts are being subsidised by the network's most efficient customers.
Tight delivery time windows (e.g. 2-hour slots) can increase the cost-to-serve of a route by 30-50% compared to flexible delivery - yet this is rarely reflected in pricing. Customers with strict time window requirements should pay a service premium.
A customer placing 20 orders per month at 1 pallet each costs far more to serve than a customer placing 2 orders per month at 10 pallets each - even with the same total volume. Order frequency is one of the most powerful cost drivers in distribution, and it is often invisible in standard pricing.
Before building a full TDABC model, we recommend starting with the Profitability Health Check - a 12-question diagnostic that takes 5 minutes and benchmarks your current maturity across all 7 dimensions. It tells you where to focus first and what level of improvement is realistic given your current data and process maturity.
Take the free Profitability Health Check - logistics version - and find out where your distribution network is losing margin.
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